There is only one boss. The Customer. And he can fire everybody in the company from the chairman on down, simply by spending his money somewhere else. – Sam Walton.
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Every good company can survive only providing excellent customer service. But with the evolution of technology on a day to day basis, the customer expectations are soaring high. Hence to balance it with other business needs and goals, it is a paramount task. Excellent customer satisfaction gives the critical competitive edge for any business nowadays.
With data becoming the raw material for customer satisfaction, it has become the prime focus of digital business now. The rapid pace of its accumulation has created new opportunities and challenges for companies.
Only by analyzing these mountains of collected data could enable to predict the customer behavior for better results. Almost all the new and resurging technologies solely depend on data. It includes AI, IoT, AoT, ML, RPA, AR, and many more for better customer satisfaction.
This year there will be more focused on data-driven technologies for a better future. It is now considered not only an ideal but the expectation of the modern business world. The recent past few years have seen the data explode to enormous levels. The evolution of business intelligence from spreadsheet work is now in the cloud.
Data analytics democratized the data product chain. With evolving BI landscape, its strategies are going to be more customized. 2019 is going to be a year to look beyond the hype of data and find ways and means to extract the maximum possible scalable and secure benefits from it.
Master data management will become the top priority of the business intelligence strategies of organizations. The data discovery trend will become one of the best BI trends as humans’ process visual data better. Data analytical tools are a boon to this data-driven business world. The future trend is using intelligent business analytical tools for the effective decision-making process.
It will become the customers’ prerogative to dictate the advancements and future trends for thriving businesses in the future. It is because they are now more asking what the best BI solution for their specific business is rather than the need for it is.
Data Analytics and Business Intelligence Trends
To fire any business in this technological world, fuel in the form of data analytics and business intelligence is essential. The following are 6 of the best trends that could add fuel to the fire of future business development and growth.
1. Consumer Experience
“Excellent customer service is the number one job in any company! It is the personality of the company and the reason customers come back. Without customers, there is no company!”- Connie Elder
Gartner has recently developed a Hype Cycle for supporting the customer process by analyzing them with critical technologies. It is because many business leaders are relying now on technology to deliver the desired customer experience. Future trends in customer experience will depend on collecting quality data rather than massive data.
According to Bill Gates, “your most unhappy customers are your greatest source of learning” makes the customer journey easy with many analytics. Only by trial and error, you can give the perfect customer experience can through multiple devices and channels in this digitalized world. Some of the customer journey analytics include:
- Emotion detection
- Speech analytics
- CEC or the customer engagement center
- Interaction analytics
- Customer intelligence analytics
“By 2021, 15 % of all customer service interactions will be completely handled by AI, an increase of 400% from 2017”.
The above prediction of Gartner in 2019 Strategic Technology Trends report is a significant indicator of AI influence in customer experience. It promulgates AI into future customer experience enhancements for better customer satisfaction. AI and its subfield Machine learning are revolutionizing businesses and data management.
The benefits of AI for better customer experience:
- Live dashboards help businesses not only to monitor every second happening but also to get alerts when something is not right
- Algorithms based on advanced neural networks to provide high accuracy in anomaly detection
- Automated analyze of datasets to provide high-quality insights and a better understanding of information even without an IT background.
- Online verification processes like CAPTCHA technologies are enabled with GANs or Generative Adversarial Networks to determine the image is artificial or not.
2. Data-Driven Future
“Not everything that can be counted counts and not everything that counts can be counted.” – Albert Einstein
In this data world, the above words of Einstein imply upon the right data. All data are not practical and usable and hence to analyze for the appropriate data is the need of the hour. What information is essential as per the business needs has to be given preference for business sustainability and success in the long run.
As per reports, more importance and focus will be on data quality management and data discovery. The use of data to predict customer behavior is becoming crucial in today’s digital world. Also, strict data quality management enables us to comply with the recent stringent regulations and demands.
The future business intelligence trends will upraise not only data management but also safeguard the organizations’ initial investments. It also improves the ability to leverage business intelligence to gain a competitive edge over. It makes them maximize the returns on the BI investment and growth of the organizations.
As per Gartner, poor quality of data costs organizations an annual average loss of more than 15 million dollars. Since the information being the backbone of businesses today, their bad condition is affecting organizations in many ways. It reduces the accuracy of understanding customers aspirations and behavior which is crucial for business decisions
Last year focused more on the analytical trends in data quality. In 2019 it is set to gain momentum by way of extracting value from the massive data, Hence as per a survey, it is decided to give importance to quality data gathering than vast volumes of data. It is the focus of business intelligence for the future.
Many organizations have started data quality management policy departments for efficient data analysis DQM comprises acquiring, implementing, distributing, and effectively managing the data for innovative insights and successful decisions. Data management to find the right data for business success is correct as per the adage below:
“He would search for pearls must dive below.” – John Dryden
It is one of the top three in the importance hierarchy of business intelligence trends by the Business Application Research Center. It had a rapid improvement last year and set to gain far more pace this year to be a reliable and consistent trend of the future. Data discovery is essential to businesses as per the adage below of W.Edward Deming:
“If you do not know to ask the right question, you discover nothing.”
Data discovery tools to generate findings and bring business value needs to depend on a process. It involves a proper understanding of the relationship between data preparation and precise advanced analytics. Business leaders can make quick decisions by instantly identifying major trends with interactive and new visualization types of analyzing tools.
For this kind of data visualization tools, they must get equipped with the appropriate software that makes using easy, more flexible and agile to reduce time to insight and facilitating handling even high volume with a lot of varieties efficiently.
Hence, a highly resourceful tool enables to provide the relevant insights to create successful and sustainable decisions.
With recent undesirable experiences of Google and Facebook of a data breach, the consumers’ awareness is high now. They are now more concerned about their personal information and online habits.
GDPR of Europe and CCPA of California sets the precedent of data protection strategies. It has become essential for app developers to know about GDPR Compliance. It has made Gartner state for data compliance:
“The conversation should move from ‘Are we compliant?’ toward ‘Are we doing the right thing?”
3. The Growing Importance Of CDO & CAO
In today’s troubled data management world, even many conglomerates are now focusing more on compliance regulations. It has increased the importance of CDOs and CAOs or the Chief Officers of Data and Analytics. These posts are now considered the most robust seats in the executive table of organizations because of their enormous responsibilities.
Gartner, in their 2019 Summit in Orlando has implemented CAO as the hot topic. Business intelligence market trends incorporate CAO role to add value and increase significance to businesses. The purpose of CDO is to perform the following functions to get a competitive edge in their business intelligence strategy:
- To improve data analysis effectively to drive value from data
- Proper management of the company’s information assets
- Develop a system to leverage data across all business units within the organization including marketing, sales, procurement, and finance
- Supervise all the information management and security issues
- Empower all the users with clean, trusted and ready to use data
- To enhance focused outcomes, they should ensure maximum value extraction.
CDO VS CAO:
The chief role of CAO or the chief analytic officer is to drive insights into the data to make it actionable. CAO complements the roles of CDO and CIO or the previous principal information officer role. CAOs are fast becoming a predominant skill set; many companies are recruiting for their data analysis into actions.
4. Data Governance – Trust, Security & Digital Ethics and Privacy
Off late database, security has become a heated topic due to various recent undesirable occurrences. Both the public and private organizations have to improve safety at an unprecedented pace in 2019. Many business leaders and entrepreneurs are always in search of the best and safest solutions for their organizations to avert data breach and loss riks.
According to Gartner predictions for 2019, the organizations should increase trust and reliability in their data analytic practices. For this, it has recommended the following data governance measures have to be in place by 2019 for better and secure data management:
- Increased collaborative processes to help both IT teams and end users
- To make them both agree and implement data governance models.
- Without jeopardizing the security to maximize the business value of analytics
- Increase the need for organizations to view data governance as a critical necessity than a regular procedure
- Increase the experience of organizations to overcome challenges
- To obtain reliable business values by implementing and combining data quality, risk, ethics, Privacy and security
As per the definition of DGI or the Data Governance Institute, data governance is “the exercise of decision-making and authority for data-related matters.” It means that particular standards should control over any data entry. All this will make organizations vigilant in data governance and quality in the future.
Data is only useful when it is appropriately analyzed and easily accessible. Hence organizations have now started to focus on maintaining a balance between data access and security.
Also, they have to be agile and adapt as per the changes in businesses. It not only concerns big companies but also nowadays involves smaller companies. Newly invented data preparation tools and methods are now a big help in fueling this trend of data governance and security. In the process, they also reduce the cultural gap between technology and business. With centralized, clean, and fast data sources organizations can quickly dig into their data without any security concerns.
5. Business Analytical Tools
To answer the ultimate questions of what will happen? How can we make it happen, and many more are focused on business analytics. It makes predictive and prescriptive analytics the most discussed trends by BI professionals. It gains momentum with big data being not only leveraged by big enterprises but also SMBs alike.
Predictive analytics forecasts future probabilities from existing data. It indicates alternative scenarios of what might happen in the future of potential risks and opportunities of organizations to a certain level of reliability.
Uses of predictive analytics in businesses:
Many industries use predictive analytics in many ways, and there are several significant data examples. They are used in real life to change the world a better place to live.
It could be in the way of buying experience or managing customers; it is there everywhere. All kinds of industries today use it for various purposes, which include:
- Airline industry uses it to decide to sell of how many tickets for a flight at what price
- Hotels use it to adjust prices, analyzing the number of guests expected for any given day to maximize occupancy and increase revenue.
- Marketers use it for cross-selling opportunities by analyzing customer responses or purchases.
- Banks generate credit scores by incorporating all the relevant data to a person’s creditworthiness.
Prescriptive analytics gives the decisions and steps to be taken to reach the goals. It tries to foresee the effect of future decisions to adjust the choices before implementation. It enables better decision making is made considering the future outcomes. The following techniques characterize it:
- Graph analysis
- Complex event processing
- Neural networks
- Recommendation engines
- Machine learning
The prescriptive analysis helps in the following:
- Improved decision making with future outcomes taken into consideration
- Optimize scheduling
- Production inventory
- Supply chain designs to deliver customer requirements in an optimized way
Analytics of Things
AoT or Analytical of things is the sequel to the Internet of Things in terms of popularity in recent years. The tons of data that IoT generates are analyzed by AoT to make decisions as per business needs. It enables connected devices smarter to make mind-boggling decisions. IoT becomes meaningful and efficient by AoT of analyzing its data.
Since IoT itself is only making inroads in the last few years, AoT is only at an infancy stage. The major challenge of AoT is storing the real-time data that IoT generates.
Each sensor of IoT generates extensive data, and managing it is a difficult task. Today’s businesses face tasks of avoiding junk data, ensuring data privacy, and protecting the data at private places.
With security analytics organizations and Governments instead of waiting for a cyber-attack could eliminate its possibilities. Since in recent days the talk of the town is a cyber attack, Analytics could proactively identify possible threats. Its timely detection and alleviate such attacks in the future. Also, it enhances privacy policies.
Big Data security analytics brings out from the mountains of data the hidden relationships in the inside and outside of organizations. It enables law enforcement across the globe to identify threats and also collect evidence of it by analyzing the data from the internet, smart devices, and social media. Security analytics is a boon to organizations for improved protection from cyber attacks.
Business intelligence cannot be far away when the data and apps are en-routed towards cloud analytics. Nowadays, it is not only the sky is full of cloud, but it is also surrounding the business world for better. In the time of fear giving way for the cloud to take over the businesses to migrate most of the elements to the cloud. It includes:
- Data sources
- Data models
- Processing applications
- Computing power
- Data storage
Also, some examples of cloud analytics products and services include:
- Hosted data warehouses
- SaaS Business Intelligence tools
- Cloud-based social media analytics
The future powered by Analytics real-life technology is more rivaling and even outweighs everything we have seen in hi-fi Hollywood movies. It is both thrilling and exciting, waiting to watch more of what these trends are going to unfold. Also the way they are going to change the world and its method of living in the future.
6. Collaborative Business Intelligence
“When there are teamwork and collaboration, wonderful things can be achieved.” – Mattie Stepanek
The Gartner report states that collaboration information, its enhancement, and decisions are the principal focus of new BI tools. But it should not end up only with documents and updates. It should be continuous tracking of all the progress of businesses, including meetings, calls, e-mail exchanges, and the collection of ideas.
With today’s change in need of interactions between managers and workers in this competitive business world, collaborative BI is fast rising. It is a successful combination of collaboration tools like social media and 2.0 technologies with online BI tools. It enhances the way analysis in this fast track business world.
Collaborative BI makes sharing easier in generating automated reports as per schedule time and people. It also enables business with many functions accessible to all types of devices. The features include:
- Intelligence alerts
- Share public dashboards
- Share embedded dashboards with a flexible level of interactivity
Evolution of Self-Service BI:
One part of Collaborative BI is “Self Service BI,” and its tools do not require any human effort to analyze data. The interchangeability of it with visual data discovery is gaining popularity now.
Business decisions with reduced errors are possible with visual data discovery. BI, as a service, offers three critical functionalities for its users, which include:
- Extracting and desegregating data from massive databases
- Organizing data into a high-performance data warehouse
- The capability of accessing and processing the data through purpose-built interfaces and apps
Self-service BI is used mainly by ordinary people has a simple user interface with a dashboard and user-friendly navigation. Even non-technical people could benefit from it to create and access BI reports, queries, and analytics.
Differences between cloud BI and On-Premise BI:
- Short implementation time vs. generally longer time
- Low upfront investments vs. high investments
- No additional hardware or IT costs vs. high investments.
- Predictable one-time costs vs. unpredictable all time costs
- Less customizable in general vs. more exceptional ability to customize
- Vendor control of data security standards vs. organization controls
Quality data to replace junk data:
The future data analytics and intelligent business trends to fire up businesses need quality data in place of junk data. It is going to be the thumb rule for future emerging trends.
All the data analytics and business intelligence aim in providing quality data with its highest security. Only this could make the future of business better with best business decisions from the right insights from the quality data through proper analysis of it.
Discover, manage & governance of data for better future:
The first future data analytics and business intelligence trends involve data discovery, management, and governance with proper security. Data doubling every two years and on a rapid pace of accumulation has to be handled properly.
The new posts of CAO and CDO or the chiefs of analytics and development officers added to the CIO or the chief information officer of organizations. It enables better discovery, management, governance, and security of data for its better usage by organizations.
Best customer experience the top trend of data analytics and BI:
The customer is God or king or the boss and everything for a company to sustain, develop, and succeed. All trends and technologies only aim at this point of best customer experience for their fullest satisfaction.
Now, AI is into the fray of providing the best of customer experience with automation and making machines learn with machine learning.
Also, collaborative business intelligence, coupled with best customer experience is the most favored trends of the future. It has made many top mobile development companies and mobile app developers to opt for the latest trends of data analytics and business intelligence.